Video advertisements are among the most advanced, complicated, and expensive, forms of advertising content. Beyond the costs to produce video content itself, the expense of delivering video content over the broadcast and cable networks remains considerable, in part because television (TV) slots are premium advertising space in today's economy. Furthermore, TV is no longer a monolithic segment of the media market. Consumers can now spread their viewing of video content, particularly premium content, across TV, DVR, and a menagerie of over-the-top and on-demand video services viewed across smart TVs, gaming consoles, and mobile devices, as well as traditional TVs.
In short, TV viewing is transforming to digitally distributed viewing, as audiences watch proportionately less live broadcasting and more in a video on demand (VOD) or streaming video format. Correspondingly, distributors of TV content are looking more and more to harness digital data from TVs themselves to offer more attractive placement options to advertisers.
Adding online consumption to the list of options available to any given consumer, only adds greater complexity to the process of coordinating delivery of video adverts to a relevant segment of the public. This complexity means that the task of optimizing delivery of advertising content today far exceeds what has traditionally been necessary, and what has previously been within the capability of experienced persons. The data needed to fully understand a given consumer is fragmented as each individual and household views more and more media in a disparate fashion by accessing a network of devices.
Nevertheless, for many companies, the analytical work that goes into developing an advertising strategy today still requires manual contributions by human analysts. This is especially the case for low volume purchasers of advertising inventory, and is largely the case for advertising purchases on “linear TV”—TV content that is pre-scheduled (often many weeks or months in advance), as opposed to video-on-demand.
Advertising strategies are also generally fixed, meaning that approaches to advertising strategy are conditioned on certain assumptions that are inflexible and limited to what manual processes are able to achieve. The current state of advertising strategy is analogous to where financial trading was before the creation of financial strategy tools such as E-Trade, which facilitates automated buying, and financial advisors such as Fidelity for investment planning.
In implementing today's advertising strategies, human analysts guide the selection of advertising inventory based on, for example, Excel data tables and other static data management tools. This results in inefficient selection of slots, and delays in responding to market trends. Consumers are not disparate silos of preference based on the device they are using, but the market for advertising treats them that way due to limitations in the available technology tools, most of which are incapable of quickly and accurately integrating disparate data sets. For example, today, TV consumption data exists separately from set top box owner data and TV OEMs. It follows that advertising strategies for TV are planned according to TV-specific criteria, and web and mobile advertising, which include sub categories such as social media, are each planned separately. Across the advertising industry, there are separate entities planning for different media platforms, such as set-top box, phone and desktop. Across the different media there exists disparate data, data systems, and data sources (vendors). Today, these device and media categories remain largely segmented when incorporated into advertising campaign strategies and planning. Nevertheless, the increasing availability of digital TV data, often referred to as “programmatic TV” data, means that advertisers are beginning to be able to be more sophisticated about their purchases of TV ad slots.
Currently, some companies attempt to link together a set of devices related to a particular consumer, but they are not capable of treating the disparate data sources with any reliable level of data integration, or at a scale that is useful to advertisers. Identifying selections of devices by comparing and modeling incomplete user data against that of other similar users within the market segment is a partial solution to this issue, but current methods are not able to create associations at a level of granularity that is reliable or useful.
Today, probabilistic and deterministic methods are not widely utilized to associate mobile and computer devices to a precise audience, or household. One reason such methods are not more widely adopted is because of inefficient processing and pairing of data across different devices. For example, in order to predict consumer purchasing, viewing, and advertising interaction habits at a 1:1 level of an association between a user and their respective device, it is insufficient to assume that any single instance of device access is representative of that user's purchasing intent. This is due to modern day habits of media consumption—users consume media on a large variety of devices as well as via different media (such as Hulu, Netflix, or cable television). As such, a much more complex analysis that enables insight into the intersection of media consumption and a user's family of devices is necessary.
Another reason probabilistic and deterministic methods are not more readily available to gauge consumer purchasing habits is because access to user device data is not easily achieved. For example, under consumer privacy laws, it is unlawful to access a user's device without their explicit consent. Therefore, on a mass scale, it is often unknown what combination of devices a demographic of users use, and what media they consumed on their respective devices. This poses a large challenge for advertisers when determining which advertising inventory to purchase and how best to reach their target audience efficiently on a given category of device, in particular the TVs they watch.
Today, the data systems that track consumer information for use in advertising targeting lack the ability to broadly combine and integrate transmutable and non-transmutable categories (i.e., requiring the integration of a plurality of membrane levels) of consumer data. Most data systems contain static, one-dimensional, homogeneous classifications of consumers. For example, a 29-year old who bought a car two years ago will be a consumer data point that will not adjust or be updated over time. While adjusting the age of this individual over time is simple, other transmutable characteristics such as desire to get married, pregnancy, or other lifestyle changes are not easy to assess or predict. This translates to an inefficiency of advertising placement, and also means that even if an advertiser can target online content to that consumer, their ability to reach them on the TVs they view is severely restricted.
Accordingly, there is a need for a method of integrating and connecting data on a given consumer that is acquired over time from multiple different devices, and to use that integrated data in making reliable placement of advertising content across multiple devices, in particular for TV advertisement slots.
The discussion of the background herein is included to explain the context of the technology. This is not to be taken as an admission that any of the material referred to was published, known, or part of the common general knowledge as at the priority date of any of the claims found appended hereto.
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